Statistical Methods in Data Science and Machine Learning (DSML) 

Due to time zone differences, this program might be best suited for students in China and the US.

Statistics plays a central role in both data science and machine learning. We will introduce a set of most popular statistical methods in data science and machine learning for various tasks, such as classification and regression. These will include very modern techniques such as ensemble tree-based methods (including bagging, random forests, boosting such as gbm and xgboost, etc.) and deep neural networks, and also the ever-popular classical techniques such as nearest neighbor classifiers, discriminant analysis, and naïve Bayes. We will also discuss important thoughts such as curse of dimensionality and bias-variance tradeoff. Students will learn not only what the methods are, how they work, and why they work, but also how to code and use them in real data.

Program Features
  • An academically rigorous online program that incorporates 28 classroom contact hours with faculty, 9 hours of workshops, 5 hours of virtual events and activities, and 7.5 hours with teaching assistants.  
  • Live and synchronous classes with a University of Notre Dame professor in the department of Department of Applied and Computational Mathematics and Statistics.
  • A specially designed schedule that accommodates time differences for students in China/Asia, with classes in the morning and workshops & events in the evening, as well as an 4-day break for Chinese New Year.
  • Live workshops on Applying to Grad School in the U.S. & at Notre Dame, Academic English Presentation Skills, Intercultural Communications, etc. 
  • Sessions of live discussions with Notre Dame students. 
  • Panel discussion with Notre Dame’s current international Ph.D students. 
Tentative Program Schedule

For your reference only. Class meeting times might shift during the actual program. A finalized schedule will be provided to all participants in late January. 

Data Science & Machine Learning Tentative Program Schedule

Delivery Method

100% online and synchronous (live) 


15 days of classes 


Monday, Feb 1st - Friday, Feb 19th, 2021. No class between Feb 11 - Feb 14.  

  • International undergraduate students enrolled in any accredited, non-Notre Dame institution in the United States or abroad.  
  • Minimum GPA: 2.75 
  • Preferred English proficiency:
    • TOEFL iBT "My Best Score" 80 
    • IELTS 6 
    • Duolingo English Test 105 
    • Chinese English Test 4 (CET4) 500
    • Chinese English Test 6 (CET6) 450
Preparation for This Course
  • Basic knowledge of applied probability and applied statistics, especially, linear regression.
  • R programming. Suggested read - click here ( Chinese version).

Submit your application using the link below. 

PLEASE NOTE: You will be required to upload a copy of your English proficiency test report and a copy of your university transcript. Both reports can be unofficial, but must be legible and with your name on the report.

Apply here

Application Deadline

January 5, 2021

Program Fee



Admitted students will receive detailed instructions on making the program fee payment (via credit card) once their application is submitted and reviewed.  

Refund Policy
  • Withdraw on or before January 15th - full refund.
  • Withdraw between January 16th to 21st - $100 withdrawal fee.
  • Withdraw between January 22nd to 31st - $200 withdrawal fee.
  • Withdraw on or after February 1st (program start date) - no refund.
Attendance Policies

Enrolled participants are expected to attend all classes and workshops. Personal emergencies such as illness or technological issues can be excused and program staff must be notified. 

Program Certificate

Upon successful completion of the program, each participant will receive an official program completion certificate. 

Program Invoice

Some students might wish to apply for scholarships at their home universities to cover their program fee. For this purpose, an official program invoice can be provided upon request. Please email to request your invoice. 


Please email for any questions.